| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Explainable AI Model Interpretability Feature Importance Transparency Responsible AI |
About the Introduction to Explainable AI Course
The Introduction to Explainable AI course is a free, beginner-friendly self-paced program designed to help learners understand how artificial intelligence models make decisions and how those decisions can be interpreted and explained.
The course introduces key concepts such as model transparency, interpretability, trust in AI systems, and the importance of explainability in real-world applications. Learners will explore why understanding AI decisions is critical in areas like healthcare, finance, and business. This course is ideal for beginners who want to understand how AI systems can be made more transparent and trustworthy.
Program Highlights
• Free beginner-level Explainable AI course
• Online self-paced learning format
• Simple explanation of AI interpretability concepts
• Covers transparency, trust, and model understanding
• Real-world examples from healthcare, finance, and technology
• Suitable for technical and non-technical learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Explainable AI
- What is Explainable AI (XAI)?
- Why Explainability Matters in AI
- Black Box vs Interpretable Models
- Applications of Explainable AI
Module 2: Understanding AI Decisions
- How AI Models Make Predictions
- Concept of Model Outputs and Features
- Importance of Transparency
- Trust and Reliability in AI Systems
Module 3: Basic Explainability Techniques
- Feature Importance Concepts
- Local vs Global Interpretability
- Simple Explanation Methods
- Understanding Model Behavior
Module 4: Responsible and Ethical AI
- Explainability and AI Ethics
- Bias, Fairness, and Accountability
- Role of XAI in Decision-Making Systems
- Limitations of Explainable AI
Module 5: Applications and Future Scope
- Explainable AI in Healthcare, Finance, and Business
- Regulations and AI Governance Basics
- Career Opportunities in Responsible AI
- Mini Learning Activity / Concept-Based Practice
Tools, Techniques, or Platforms Covered
Explainable AI Model Interpretability Feature Importance Transparency Responsible AI
Real-World Applications
- Understanding AI decisions in healthcare diagnosis systems
- Explaining loan approval decisions in finance
- Improving trust in recommendation systems
- Supporting transparent AI in business and policy
- Preparing for advanced learning in responsible AI and ML
Who Should Attend & Prerequisites
- This course is suitable for students, beginners, professionals, and anyone interested in understanding how AI decisions can be explained.
- It is also useful for learners from data science, business, healthcare, finance, and policy backgrounds.
Certification
